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PUBLICATIONS

Please refer to my Google Scholar profile and ORCID page for details.

Peer-reviewed Articles

  1. Song, I., & Luan, H. (2024). Localized effects of neighborhood park exposure on mental illness mortality in the Pacific Northwest United States. Applied Geography. 162, 103127. https://doi.org/10.1016/j.apgeog.2023.103127

  2. Ransome, Y., Luan, H., Song, I., & Duncan, D.T. (2023). Church closings were associated with higher COVID-19 infection rates: Implications for community health equity. Journal of Urban Health. https://doi.org/10.1007/s11524-023-00791-2

  3. Song, I., & Kim, D. (2023). Three common machine learning algorithms neither enhance higher prediction accuracy nor reduce spatial autocorrelation in residuals: An analysis of twenty-five socio-economic data sets. Geographical Analysis 55(4), 585-620. https://doi.org/10.1111/gean.12351

  4. Nguyá»…n, C., Song, I., Jung, I., Choi, Y.-J., & Kim, S.-Y. (2023). Changes in spatial clusters of cancer incidence and mortality over 15 years in South Korea: implication to cancer control. Cancer Medicine 12(16), 17418-17427. https://doi.org/10.1002/cam4.6365

  5. Song, I., Yoo, E.-H., Jung, I., Choi, Y.-J., & Kim, S.-Y. (2023). Role of geographic characteristics in the spatial cluster detection of cancer: evidence in South Korea, 1999-2013. Environmental Research 236, 116841. https://doi.org/10.1016/j.envres.2023.116841

  6. Kim, D., Song, I., Miralha, L., Hirmas, D.R., McEwan, R.W., Mueller, T.G., & Šamonil, P. (2023). Consequences of spatial structure in soil–geomorphic data on the results of machine learning models. Geocarto International 38(1), 2245381. https://doi.org/10.1080/10106049.2023.2245381

  7. Taggart, T., Ransome, Y., Andreou, A., Song, I., Kershaw, T., & Milburn, N. (2023). Utilizing activity space assessments to investigate neighborhood exposure to racism-related stress and related substance use risk among young Black men. American Journal of Public Health 113(S2), S136-S139. https://doi.org/10.2105/AJPH.2023.307254

  8. Jun, Y.-B., Song, I., Kim, O.-J., & Kim, S.-Y. (2022). Impact of limited residential address on health effect analysis of predicted air pollution in a simulation study. Journal of Exposure Science and Environmental Epidemiology 32, 637-643. https://doi.org/10.1038/s41370-022-00412-1

  9. Song, I., & Luan, H. (2022). The spatially and temporally varying association between mental illness and substance use mortality and unemployment: a Bayesian analysis in the contiguous United States, 2001-2014. Applied Geography 140, 102664. https://doi.org/10.1016/j.apgeog.2022.102664

  10. Ransome, Y., Luan, H., Song, I., Fiellin, D.A., & Galea, S. (2022). Poor mental health days are associated with COVID-19 infection rates in the USA. American Journal of Preventive Medicine 62(3), 326-332. https://doi.org/10.1016/j.amepre.2021.08.032

  11. Luan, H., Song, I., Fiellin, D. and Y. Ransome. (2021). HIV infection prevalence significantly intersects with COVID-19 infection at the area-level: a USA county-level analysis. Journal of Acquired Immune Deficiency Syndromes 88(2), 125-131. https://doi.org/10.1097/QAI.0000000000002758

  12. Kim, D. & Song, I. (2021). Predicting model improvement by accounting for spatial autocorrelation: A socio-economic perspective. The Professional Geographer 73(1), 131-149. https://doi.org/10.1080/00330124.2020.1812408

  13. Song, I., Kim, O.-J., Choe, S.-A., & Kim, S.-Y. (2020). Spatial heterogeneity in the association between particulate matter air pollution and low birth weight in South Korea. Environmental Research 191, 110096. https://doi.org/10.1016/j.envres.2020.110096

  14. Park, Y., Song, I., Yi, J., Yi, S.-J., & Kim, S.-Y. (2020). Web-based visualization of scientific research findings: national-scale distribution of air pollution in South Korea. International Journal of Environmental Research and Public Health 17(7): 2230. https://doi.org/10.3390/ijerph17072230

  15. Kim, D., Lee, J.-Y., Seo, J., & Song, I. (2019). Recolonization of native and invasive plants after large-scale clearance of a temperate coastal dunefield. Applied Geography 109, 102030. https://doi.org/10.1016/j.apgeog.2019.05.007

  16. Song, I., Lee, C., & Park, K.-H. (2018). An ensemble machine learning from spatio-temporal Kriging for imputation of PM10 in Seoul, Korea. Journal of the Korean Geographical Society 53(3): 427-444. https://journal.kgeography.or.kr/articles/pdf/vMLR/geo-2018-053-03-9.pdf

  17. Kim, S.-Y., & Song, I. (2017). National-scale exposure prediction for long-term concentrations of particulate matter and nitrogen dioxide in South Korea. Environmental Pollution 226(2017): 21-29. https://doi.org/10.1016/j.envpol.2017.03.056

  18. Song, I., & Kim, S.-Y. (2016). Estimation of representative area-level concentrations of particulate matter (PM10) in Seoul, Korea. Journal of the Korean Association of Geographic Information Studies 19(4): 118-129. (in Korean, English abstract available) https://doi.org/10.11108/kagis.2016.19.4.118

  19. Eum, Y., Song, I., Kim, H.-C., Leem, J.-H., & Kim, S.-Y. (2015). Computation of geographic variables for air pollution prediction models in South Korea. Environmental Health and Toxicology 30: 70-83. https://doi.org/10.5620/eht.e2015010

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Other publications

  1. Ransome, Y., Song, I., Pham, L., & Busette, C. (2022). Churches are closing in predominantly Black communities – why public health officials should be concerned. The Brookings Institution. Published May 3, 2022.

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